I’m stoked to share our new paper: “Harnessing the Universal Geometry of Embeddings” with @jxmnop, Collin Zhang, and @shmatikov.
We present the first method to translate text embeddings across different spaces without any paired data or encoders.
Here's why we're excited: 🧵👇🏾
It's deeply concerning that one of the best AI researchers I've worked with, @kaicathyc, was denied a U.S. green card today. A Canadian who's lived and contributed here for 12 years now has to leave. We’re risking America’s AI leadership when we turn away talent like this.
Starting salaries at Cambridge are less than $70k for most fields. Stanford? Double that.
As someone trying to hire the best global talent to work on UK and EU projects, I can't compete with the American universities. One election won't change that.
a reason why it’s painful for academics to be on X is the default academic epistemic humility mode we like to talk and write in does not work on this platform where shitpoasters speak/write as if they dug up a tablet and god spoke to them about everything.
We should call models like Llama 3, Mixtral, etc. “open-weight models”, not “open-source models”. For a model to be open-source, the code and training data need to be public (good examples: GPT-J, OLMo, RedPajama, StarCoder, K2, etc.). Weights are like an exe file, which would be ridiculous to call open-source.
That AAAI felt compelled to put an upper bound on the number of papers (ten!) from an author should tell you something about the state of AI publishing today.
We remain committed to our partnership with OpenAI and have confidence in our product roadmap, our ability to continue to innovate with everything we announced at Microsoft Ignite, and in continuing to support our customers and partners. We look forward to getting to know Emmett Shear and OAI's new leadership team and working with them. And we’re extremely excited to share the news that Sam Altman and Greg Brockman, together with colleagues, will be joining Microsoft to lead a new advanced AI research team. We look forward to moving quickly to provide them with the resources needed for their success.
Launching today: AI-powered candidate feedback summaries in Rippling Recruiting 🚀
This new feature auto-synthesizes interview feedback into a digestible, helpful snapshot of each job candidate.
Stay tuned—more AI-driven features are coming soon🛠️
https://t.co/nmVZxc5mPe
We investigate what features of tabular data explain the difference, for this, we modify tabular data to narrow the gap.
Smoothing the outcome in feature space narrows the gap: deep architectures struggle with irregular patterns. Tree models do not care about smoothness
6/9
A lot has been speculated about TikTok's recommendations. This is the first paper I've read by the team, and it has many interesting details: expirable embeddings, parameter server, online training... Good #recsys stuff https://t.co/r8UBCrnZi8
What I learned over the years is that people outside of the machine learning research community are so much better at UI/UX, design, web-dev, creating apps and plugins for all sorts of platforms, and making ML actually useful to a large number of real users.
📢Announcement📢: After careful consideration, we have decided to host AISTATS 2023 in Valencia (Spain), rather than a location in the US. Valencia was the original planned location for the 2022 conference, which had to be moved online. See you in Valencia!
Neural rendering takes its next step with DLSS 3.0 on Ada! In addition to DL-powered superresolution, it uses optical flow, motion vectors, and DL to generate entire frames.
7 out of 8 pixels being rendered with DLSS3 come from Neural rendering. #GTC22
I made an accessibility map of #Neurips2022. The country is marked in red if its resident can not get appointment in time from getting accepted to the conference (76 days).